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Health Monitoring in Sport Through Wearable Sensors: A Novel Approach Based on Heart-Rate Variability

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Mobile Networks for Biometric Data Analysis

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 392))

Abstract

Sudden cardiac death (SCD) is one of the leading cause of death during sport activities. Heart rate (HR) and HR variability (HRV) provide a measure of how the organism adapts to physical fatigue, and can be monitored by commercial wearable sensors. Still, HR and HRV, widely used to optimize a training session, were rarely used to evaluate the athlete’s health-status, even though widely known to provide indexes of risk for SCD. This work, developed in collaboration with Bio-Medical Engineering Development Srl, aims to provide a contribution to the problem of preventive identification of athletes at increased risk of SCD, by developing and testing a low-cost, large-scale procedure for HR and HRV monitoring from signals obtained using comfortable wearable sensors. To this aim a new protocol for the acquisition of the tachogram was proposed. It included recordings of the signals during resting, exercise and recovery phases, to allow evaluation of prevention as well as performance indexes. The procedure was tested on 10 sedentary subjects (SS) and 10 amateur athletes (AA). Compared to SS, AA showed a better health-status, quantified in a lower resting HR (63 bpm vs. 73 bpm; P < 0.005) and a higher resting HRV (29 ms vs. 23 ms; P < 0.05), and a better performance level, quantifies in a lower recovery time (130 ms vs. 174 ms; P < 0.05). Thus, the proposed procedure allows evaluation of both the health-status and the performance level of an athlete, and represents a valuable tool to contrast SCD in sport.

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Maranesi, E., Morettini, M., Agostinelli, A., Giuliani, C., Di Nardo, F., Burattini, L. (2016). Health Monitoring in Sport Through Wearable Sensors: A Novel Approach Based on Heart-Rate Variability. In: Conti, M., Martínez Madrid, N., Seepold, R., Orcioni, S. (eds) Mobile Networks for Biometric Data Analysis. Lecture Notes in Electrical Engineering, vol 392. Springer, Cham. https://doi.org/10.1007/978-3-319-39700-9_19

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  • DOI: https://doi.org/10.1007/978-3-319-39700-9_19

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-39698-9

  • Online ISBN: 978-3-319-39700-9

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